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Writing Smarter Applications with Machine Learning
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Why do organisations fail so badly at threat detection? Despite chucking tons of cash at staff and magic next-gen ML products, detection teams rarely deliver reliable, high quality, tangible results. Where are we going so wrong? This talk will step through key issues such as re-inventing the wheel syndrome, why information accumulation/sharing matters, the traditional SOC model and detection priorities, building/retaining awesome employees and an honest look at the state of detection tooling (and often underestimated deployment hurdles). Although perhaps surprising, many issues actually have simple solutions which will be discussed through-out the talk. Technical examples will be used to quantify the challenges and how solutions can work in the real world, with lessons learnt coming straight from the experiences of the Countercept hunt team.
BSides London 2018 - Solving Threat Detection
BSides London 2018 - Solving Threat Detection
Alex Davies
AI orange belt for business managers - All about artificial intelligence
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Lecture from the the third course in the University of Washington Data Science certificate program.
Barga Data Science lecture 2
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Learn how to create an “unfair” competitive advantage for small businesses generating less than $15M/year.
AI For Your Business: An Unfair Competitive Advantage
AI For Your Business: An Unfair Competitive Advantage
Vincent Serpico
Data mining and Machine learning explained in jargon free & lucid language. By reading one can get some intuition about what data mining and machine learning is all about APPLY IT IN THEIR OWN WORK
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Data mining and Machine learning expained in jargon free & lucid language
q-Maxim
TDWI Munich 2019 What does it take to operationalize machine learning and AI in an enterprise setting? Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. It’s a long way from the environment needed to build ML applications to the environment to run them in an enterprise. Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do. This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems.
Operationalizing Machine Learning in the Enterprise
Operationalizing Machine Learning in the Enterprise
mark madsen
Machine Learning: Why Now? - ML for Executives Course. DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
DutchMLSchool. Machine Learning: Why Now?
DutchMLSchool. Machine Learning: Why Now?
BigML, Inc
BigData and Machine Learning: Usage and Opportunities for your IT department Talk presented at The Developer Conference in São Paulo - 12/0713 Mathieu DESPRIEE
Big Data & Machine Learning - TDC2013 Sao Paulo
Big Data & Machine Learning - TDC2013 Sao Paulo
OCTO Technology
introduction to AI
Artificial Intelligence (ML - DL)
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ShehryarSH1
BigData and Machine Learning: Usage and Opportunities for your IT department Talk presented at The Developer Conference in São Paulo - 12/0713
Big Data & Machine Learning - TDC2013 São Paulo - 12/0713
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Mathieu DESPRIEE
Overview of the Data Science space and a breakdown of the skill sets needed. Presented by Matt Fornito at IBM's Datapalooza Conference.
Decoding Data Science
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Matt Fornito
The outline of our course on supervised machine learning. - Laszlo kovacs Arpad Fulop
Machine learning at b.e.s.t. summer university
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Talk by Nazar Tymoshyk at NoNameCon 2019. https://nonamecon.org https://cfp.nonamecon.org/nnc2019/talk/GSRUTP/ Incident Detection & Response requires People - to Think, Tools - to provide data and analytics and Processes - to avoid fuckups and assure the quality. But with more alerts, the analysis takes more time, decisions and moreover - actions need to be taken immediately. Attackers actively use automation, so Defenders should also optimize their processes. In our presentation, we'd like to share with the community our lessons learned. Our focus would be on practical moments, the challenges we faced and the simple working solutions we discovered. We plan to challenge the audience with simple but vital questions that will help to establish a good communication bridge to make this delivery effective and valuable for engineers to improve their defense. We'd like to discuss also a variety of actions to be taken after the incident is confirmed. Come and take it.
Nazar Tymoshyk - Automation in modern Incident Detection & Response (IDR) pro...
Nazar Tymoshyk - Automation in modern Incident Detection & Response (IDR) pro...
NoNameCon
Semelhante a Writing Smarter Applications with Machine Learning
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Towards a Threat Hunting Automation Maturity Model
Say "Hi!" to Your New Boss
Say "Hi!" to Your New Boss
Intro to machine learning
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Defcon 21-pinto-defending-networks-machine-learning by pseudor00t
Defcon 21-pinto-defending-networks-machine-learning by pseudor00t
Ml 101
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San Francisco Hacker News - Machine Learning for Hackers
San Francisco Hacker News - Machine Learning for Hackers
Good++
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BSides London 2018 - Solving Threat Detection
BSides London 2018 - Solving Threat Detection
AI Orange Belt - Session 4
AI Orange Belt - Session 4
Barga Data Science lecture 2
Barga Data Science lecture 2
AI For Your Business: An Unfair Competitive Advantage
AI For Your Business: An Unfair Competitive Advantage
Data mining and Machine learning expained in jargon free & lucid language
Data mining and Machine learning expained in jargon free & lucid language
Operationalizing Machine Learning in the Enterprise
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DutchMLSchool. Machine Learning: Why Now?
DutchMLSchool. Machine Learning: Why Now?
Big Data & Machine Learning - TDC2013 Sao Paulo
Big Data & Machine Learning - TDC2013 Sao Paulo
Artificial Intelligence (ML - DL)
Artificial Intelligence (ML - DL)
Big Data & Machine Learning - TDC2013 São Paulo - 12/0713
Big Data & Machine Learning - TDC2013 São Paulo - 12/0713
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Nazar Tymoshyk - Automation in modern Incident Detection & Response (IDR) pro...
Nazar Tymoshyk - Automation in modern Incident Detection & Response (IDR) pro...
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Using functional concepts in Python. Introduction to functional programming and exploring each of the concepts, like map, filter and reduce in detail and how functional programming can help creating massively parallel software systems
Thinking in Functions: Functional Programming in Python
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Anoop Thomas Mathew
Looking to start a startup and don't know what are the important things to keep in mind? This presentation is for you. A concoction of my learnings over the past three years with building software products including many failures. Introduces the concept of startup cycle.
Protoyping Painkiller Startups
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Know the history and development of Engineering, and learn more about what world expects from engineering graduates. Targeted on Engineering Batchelor graduates to be motivated and keep focused on building innovation for the future.
ingenium
ingenium
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Web development fundamentals for complete newbies. introduction to http protocol, web servers, what is html, js and css, as well as a short demo on creating a basic web application
Web Development Fundamentals
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Anoop Thomas Mathew
We have been using web for a very long time. How does this actually work and how can I create something of my own as an informed computer software professional? How does this entire web contraption work? What are HTTP methods and status codes? Why should I care what is an web request? Why is HTML5 so popular; Is it that good? How does the flow precisely move from browser to server and back to that? What is the significance of virtual hosts? What is the cliché "cloud" anyway? Join me, let's untangle the intertwined web of mysteries together.
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Investor pitch deck for Vibe, explaining the problem and solution and traction
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Investor pitch deck for Vibe
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Learn how to use git and get accustomed with 15+ most common git commands.
Getting Started on distributed version control with git
Getting Started on distributed version control with git
Anoop Thomas Mathew
Contribute to Opensource - A tutorial on what to do and how to do for beginners.
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Advanced Computing for Sustainable Future
Advanced Computing for Sustainable Future
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Introduction to Programming and Python Diversity intended for python aspirant programmer
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Faster Python Code in 5 minutes. Quick python optimization tips for beginners and advanced users.
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First Draft of Presentation for Pycon 2013 discussing about python performance and tips and tricks on improving it.
How slow is Real slow - PyCon India 2013
How slow is Real slow - PyCon India 2013
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The Redis way of Analytics with Python. As a workshop for Fifth Elephant conference 2012.
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Redis way of Anayltics with Python - Fifth Elephant 2012
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Different companies working on different stacks, which one is the right stack for my application? Is opensource the way to go? What is the future of opensource? The Talk at FOSSMeet 2013 tries to answer these questions.
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Building a Company atop of Open Source
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Presentation for PyCon India 2012
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Investor pitch deck for Vibe
Investor pitch deck for Vibe
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Getting Started on distributed version control with git
Let's Contribute
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Advanced Computing for Sustainable Future
Ambidextrous Python - Introduction Python Libraries
Ambidextrous Python - Introduction Python Libraries
Faster Python
Faster Python
Startups and FOSS
Startups and FOSS
How slow is Real slow - PyCon India 2013
How slow is Real slow - PyCon India 2013
Redis way of Anayltics with Python - Fifth Elephant 2012
Redis way of Anayltics with Python - Fifth Elephant 2012
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Building a Company atop of Open Source
Pycon 2012 Scikit-Learn
Pycon 2012 Scikit-Learn
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Test Driven Development in Python
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Mind IT Systems
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The Ultimate Test Automation Guide_ Best Practices and Tips.pdf
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kalichargn70th171
Explore the latest in HRMS software with SHRMPro. Streamline your HR processes efficiently.
SHRMPro HRMS Software Solutions Presentation
SHRMPro HRMS Software Solutions Presentation
Shrmpro
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VTU technical seminar 8Th Sem on Scikit-learn
VTU technical seminar 8Th Sem on Scikit-learn
AI & Machine Learning Presentation Template
AI & Machine Learning Presentation Template
Writing Smarter Applications with Machine Learning
1.
Writing Smart Programs Anoop
Thomas Mathew twitter: atmb4u Profoundis Inc.
2.
History then: low level →
high level now: code driven → data driven
3.
What is Smart everyone
makes mistakes ○ looking at past to avoid future mistakes everyone misses what comes next ○ predicting what to expect clusters of items do exist ○ automatically group things based on similarity
4.
Jargon Buster Dataset Data Cleaning Dimension Model Training Parameters Dimensionality
Reduction Accuracy Overfitting Underfitting Testing Domain
5.
Parameter Optimization Heuristic / Statistical
/ Machine Learning - “know parameters well” Eg: stock market prediction disaster; no. of lawyers vs. no. of suicides
6.
Supervised vs Unsupervised we
know what we want vs. find what’s interesting NB: training data, accuracy, semi-supervised
7.
Classification / Regression
/ Clustering ★ rain prediction ★ digit recognition ★ customer segmentation ★ time-series prediction ★ spam filtering Algorithm Examples ★ K-means ★ SVR ★ SVC ★ Naive Bayes ★ Random Forest Decision Tree
8.
9.
The ML Process plan
→ collect → execute → test time: 50% 30% 5-10% 15-20%
10.
DEMO 1 SHOPPING PREDICT (https://github.com/atmb4u/smarter-apps-2016)
11.
DEMO 2 Support Vector
Machine (https://github.com/atmb4u/smarter-apps-2016)
12.
Dummy Tasks ★ user
auto-login redirect ★ predict if a user will convert to paid
13.
Thank You Follow me
on twitter:@atmb4u
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